code: def add_data_args(parser):
      def int_list(x):
          return list(map(int, x.split(",")))
  
      data = parser.add_argument_group("Data")
      data.add_argument(
          "--data-pred",
          type=str,
          help="the image on which run inference (only for pred mode)",
      )
      data.add_argument(
          "--image-shape",
          type=int_list,
          default=[3, 224, 224],
          help="the image shape feed into the network",
      )
  
      data.add_argument(
          "--input-layout",
          type=str,
          default="NCHW",
          choices=("NCHW", "NHWC"),
          help="the layout of the input data",
      )
      data.add_argument(
          "--conv-layout",
          type=str,
          default="NCHW",
          choices=("NCHW", "NHWC"),
          help="the layout of the data assumed by the conv operation",
      )
      data.add_argument(
          "--batchnorm-layout",
          type=str,
          default="NCHW",
          choices=("NCHW", "NHWC"),
          help="the layout of the data assumed by the batchnorm operation",
      )
      data.add_argument(
          "--pooling-layout",
          type=str,
          default="NCHW",
          choices=("NCHW", "NHWC"),
          help="the layout of the data assumed by the pooling operation",
      )
  
      data.add_argument(
          "--num-examples",
          type=int,
          default=1281167,
          help="the number of training examples (doesn't work with mxnet data backend)",
      )
      data.add_argument(
          "--data-val-resize",
          type=int,
          default=256,
          help="base length of shorter edge for validation dataset",
      )
      data.add_argument(
          "--kv-store",
          type=str,
          default="device",
          choices=("device", "horovod"),
          help="key-value store type",
      )
      return data
  
file_path: gluon-cv/scripts/classification/imagenet/dali.py
imports: import warnings
  from nvidia.dali.pipeline import Pipeline
  import nvidia.dali.ops
  import nvidia.dali.types
  from nvidia.dali.plugin.mxnet import DALIClassificationIterator, LastBatchPolicy
  import horovod.mxnet
label_desc: Use image node_id 2 for calling a global function with example usage: add_data_args(parser) and returns: data, list